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1 Gesellschaft für Informatik e.v. (GI) publishes this series in order to make available to a broad public recent findings in informatics (i.e. computer science and information systems), to document conferences that are organized in cooperation with GI and to publish the annual GI Award dissertation. Broken down into seminars proceedings dissertations thematics current topics are dealt with from the vantage point of research and development, teaching and further training in theory and practice. The Editorial Committee uses an intensive review process in order to ensure high quality contributions. The volumes are published in German or English. Information: ISSN ISBN This volume contains papers from the Software Engineering 2012 conference held in Berlin from February 27 th to March 2 nd The topics covered in the papers range from software requirements, technologies or development strategies to reports that discuss industrial project experience. Stefan Jähnichen, Axel Küpper, Sahin Albayrak (Hrsg.):Software Engineering GI-Edition Lecture Notes in Informatics Stefan Jähnichen, Axel Küpper, Sahin Albayrak (Hrsg.) Software Engineering 2012 Fachtagung des GI-Fachbereichs Softwaretechnik 27. Februar 2. März 2012 in Berlin Proceedings

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4 Stefan Jähnichen, Axel Küpper, Sahin Albayrak (Hrsg.) Software Engineering 2012 Fachtagung des GI-Fachbereichs Softwaretechnik 27. Februar - 2. März 2012 in Berlin Gesellschaft für Informatik e.v. (GI)

5 Lecture Notes in Informatics (LNI) - Proceedings Series of the Gesellschaft für Informatik (GI) Volume P-198 ISBN ISSN Volume Editors Stefan Jähnichen TechnischeUniversität Berlin / Softwaretechnik Sekr. TEL 12-3, Ernst-Reuter-Platz 7, Berlin, Germany Axel Küpper Telekom Innovation Laboratories / Service-centric Networking Sekr. TEL 19, Ernst-Reuter-Platz 7, Berlin, Germany Sahin Albayrak Technische Universität Berlin / DAI-Labor Sekr. TEL 14, Ernst-Reuter-Platz 7, Berlin, Germany Series Editorial Board Heinrich C. Mayr, Alpen-Adria-Universität Klagenfurt, Austria (Chairman, Hinrich Bonin, Leuphana Universität Lüneburg, Germany Dieter Fellner, Technische Universität Darmstadt, Germany Ulrich Flegel, Hochschule Offenburg, Germany Ulrich Frank, Universität Duisburg-Essen, Germany Johann-Christoph Freytag, Humboldt-Universität zu Berlin,Germany Michael Goedicke, Universität Duisburg-Essen, Germany Ralf Hofestädt, Universität Bielefeld, Germany Michael Koch, Universität der Bundeswehr München, Germany Axel Lehmann, Universität der BundeswehrMünchen, Germany Ernst W. Mayr, TechnischeUniversität München, Germany Thomas Roth-Berghofer, DFKI, Germany Sigrid Schubert, Universität Siegen, Germany Martin Warnke, Leuphana Universität Lüneburg, Germany Dissertations Steffen Hölldobler,Technische Universität Dresden, Germany Seminars Reinhard Wilhelm, Universität des Saarlandes, Germany Thematics Andreas Oberweis, Karlsruher Institut für Technologie (KIT), Germany Gesellschaft fürinformatik, Bonn 2012 printed by Köllen Druck+Verlag GmbH, Bonn

6 Software: the New Driving Force so formuliert die Business Week bereits am 24. Februar 1983 auf ihrer Titelseite. In dem dazugehörigen Artikel werden der damalige Stand der Softwaretechnik und vor allem ihre Perspektiven dargestellt. Viele der damals aufgestellten Thesen sind noch heute richtig und belegen damals wie heute die Bedeutung der Softwaretechnik. Software spielt eine zentrale Rolle bei der Nutzung innovativer Technologien und ihre Qualität ist für die Wertschöpfung der Produkte ein entscheidender Faktor. Sie muss höchsten Qualitätsanforderungen entsprechen und trotzdem kostengünstig hergestellt werden. Zuverlässigkeit, Sicherheit und Akzeptanz beim Nutzer sind Eigenschaften, die wir von moderner Software erwarten. Die Techniken zu ihrer Gewährleistung sind zwar zwischenzeitlich in hohem Maße professionalisiert und weiterentwickelt, bedürfen aber wegen der immer noch steigenden Komplexität der Systeme und der Integration mit anderen Technologien noch immer intensiver Forschung und weiterer Professionalisierung. Die Software Engineering-Tagungsreihe wird vom Fachbereich Softwaretechnik der Gesellschaft für Informatik e.v. getragen hat die Fakultät IV der TU Berlin die Gestaltung und Organisation der Tagung übernommen. Die SE 2012 bietet im Hauptprogramm sowohl eingeladene wissenschaftliche Vorträge als auch vom PC begutachtete detaillierte Berichte über laufende Forschungsarbeiten und -ergebnisse. Darüber hinaus werden in Praxisvorträgen am Industrietag aktuelle Problemstellungen, Lösungsansätze und gewonnene Erfahrungen präsentiert und zur Diskussion gestellt. Vor dem Hauptprogramm der Konferenz finden 4 Workshops sowie 3 Tutorials zu aktuellen, innovativen und praxisrelevanten Themen im Software Engineering statt. Abgerundet wird das Programm durch ein Doktorandensymposium, auf dem promovierende junge Wissenschaftlerinnen und Wissenschaftler ihre Arbeiten vorstellen und von erfahrenen Forscherinnen und Forschern konstruktive Rückkopplung zu ihren Dissertationsvorhaben erwarten. Fast schon traditionell ist ein Lehrertag in die SE 2012 integriert, auf dem sich Informatiklehrerinnen und -lehrer über neue Ansätze der Softwaretechnik für den Schulunterricht informieren. Die Durchführung der Tagung Software Engineering 2012 wäre ohne die Mitwirkung der Sponsoren und vieler engagierter Personen nicht möglich gewesen. Ich bedanke mich daher bei allen Sponsoren, vor allem aber auch bei den vielen Helfern und Helferinnen der SE 2012: - für die Gestaltung des Industrietags Axel Küpper, Ulrich Bareth und Sahin Albayrak - für die Gestaltung des Doktorandensymposiums Petra Hofstedt und Claus Lewerentz - für die Koordination der Workshops/Tutorials Holger Schlingloff und Bernhard Rumpe Der Lehrertag wurde von der GI-Fachgruppe "Informatik-Bildung in Berlin und Brandenburg" organisiert. Dank gilt hier insbesondere Helmut Witten, der als Sprecher der Fachgruppe die Koordination übernommen hat.

7 Dank auch an alle Mitglieder des Programmkommittees, des Steering Committes, an den Fachbereich Softwaretechnik und die Organisatoren bei der Geschäftsstelle der GI und der bwo Marketing GmbH. Und letztlich ganz besonderer Dank an alle meine Mitarbeiter, insbesondere Marc-Oliver Reiser, Doris Fähndrich, Marcus Mews, Andreas Mertgen und Steffen Helke. Ohne diese viele Unterstützung wäre die SE 2012 nicht möglich. Berlin, im Februar 2012 Stefan Jähnichen,Tagungsleiter

8 Tagungsleitung Stefan Jähnichen,TU Berlin/FhG FIRST Leitung Industrietag Axel Küpper, TU Berlin/T-Labs Sahin Albayrak, TU Berlin/DAI-Labor Leitung Workshops und Tutorials Bernhard Rumpe, RWTH Aachen Holger Schlingloff, HU Berlin/FhG FIRST Tagungsorganisation Doris Fähndrich,TU Berlin Steffen Helke, TU Berlin Andreas Mertgen, TU Berlin Marcus Mews, TU Berlin Mark-Oliver Reiser, TU Berlin Programmkomitee Steffen Becker, Universität Paderborn Klaus Beetz, Siemens AG Manfred Broy, TU München Bernd Brügge, TU München Jürgen Ebert, Universität Koblenz-Landau GregorEngels, Universität Paderborn Martin Glinz, Universität Zürich Michael Goedicke, Universität Duisburg-Essen Volker Gruhn, Universität Duisburg-Essen Jens Happe, SAP Wilhelm Hasselbring, Christian-Albrecht-Universität Kiel Maritta Heisel, Universität Duisburg-Essen Stefan Jähnichen,TU Berlin/FhG FIRST Matthias Jarke, RWTH Aachen Gerti Kappel, TU Wien Udo Kelter, Universität Siegen Jens Knoop, TU Wien Heiko Koziolek, ABB Claus Lewerentz, BTU Cottbus Horst Lichter, RWTH Aachen Peter Liggesmeyer, TU Kaiserslautern Oliver Mäckel, Siemens AG Florian Matthes, TU München Oscar Nierstrasz, Universität Bern Andreas Oberweis, KIT/FZI Karlsruhe Barbara Paech, Universität Heidelberg Peter Pepper, TU Berlin Klaus Pohl, Universität Duisburg-Essen

9 Alexander Pretschner, KIT, Karlsruhe Ralf Reussner, KIT/FZI Karlsruhe Matthias Riebisch, TU Ilmenau Andreas Roth, SAP Bernhard Rumpe, RWTH Aachen Thomas Santen, European Microsoft Innovation Center Wilhelm Schäfer, Universität Paderborn Klaus Schmid, Universität Hildesheim Kurt Schneider, Leibniz Universität Hannover Andy Schürr, TU Darmstadt Rainer Singvogel, msg systems AG Stefan Tai, KIT/FZI Karlsruhe Andreas Winter, UniversitätOldenburg Mario Winter, Fachhochschule Köln Uwe Zdun, Universität Wien Andreas Zeller, Universität des Saarlandes Heinz Züllighoven, Universität Hamburg Albert Zündorf, Universität Kassel Weitere Gutachter Ulrike Abelein Petra Brosch Erik Burger Alarico Campetelli Sebastian Eder Tobias George Veit Hoffmann Christian Janiesch Ruben Jubeh Andreas Koch Joern Koch Rudolf Koster Anne Koziolek Martin Küster Thomas Kurpick Achim Lindt Lars Patzina Uwe Pohlmann Hanna Remmel Jorge Ressia Johannes Rost Karsten Saller Martin Schindler Frederik Schulz Norbert Seyff Michael Striewe Nelufar Ulfat-Bunyadi Martin Wieber Dustin Wüest Gabriele Zorn-Pauli Offizieller Veranstalter Fachbereich Softwaretechnik der Gesellschaft für Informatik (GI) Mitveranstalter Technische Universität Berlin

10 Sponsoren PLATIN GOLD SILBER BRONZE

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12 Inhaltsverzeichnis Eingeladene wissenschaftliche Vorträge Thomas Santen Herausforderungenund neue Technologien zur Verifikation nebenläufiger verteilter Systeme Walter Tichy Die Multicore-Transformation und ihre Herausforderung an die Softwaretechnik Andreas Schroeder, Martin Wirsing Developing Physiological Computing Systems: Challenges and Solutions Präsentationen des Industrietages Philipp Sprengholz, UrsulaOesing Durchführung von Modultests durch den Auftraggeber in Softwareentwicklungsprojekten mittels jcut Birgit Boss SW-Architektur, SW-Sharing &Standardisierung Dehla Sokenou Softwarearchitektur für Geschäftsanwendungen auf Basis von OSGi Jan-Peter Richter, Marion Kremer TechnoVision: Neue Technologien gezielt für den Unternehmenserfolg nutzen Marc Segelken Anforderungen auf Konsistenz überprüft Formalisierung hilft Ralf Engelschall Software-Architektur und Open-Source-Lizenzrecht in Einklang bringen Simon Giesecke, Niels Streekmann Architekturmanagement für eine föderale Produktlinienarchitektur Torsten Frank Trusted Cloud im Gesundheitswesen mit TRESOR Stefan Buschner Datenschutz und Usability bei Smartcards: Card-to-Card-Authentication... 55

13 Forschungsarbeiten Dirk Riehle, Carsten Kolassa, Michel A. Salim Developer Belief vs. Reality: The Case of the Commit Size Distribution Marco Konersmann, Azadeh Alebrahim, Maritta Heisel, Michael Goedicke, Benjamin Kersten Deriving Quality-based Architecture Alternatives with Patterns Glib Kutepov Improving the software architecture design process by reusing technology-specific experience Pit Pietsch, Hamed Shariat Yazdi, Udo Kelter Controlled Generation of Models with Defined Properties Tim Frey Hypermodelling for Drag and Drop Concern Queries Wolfgang Goerigk, Wilhelm Hasselbring, Gregor Hennings, Reiner Jung, Holger Neustock, Heiko Schaefer, Christian Schneider, Elferik Schultz, Thomas Stahl, Reinhard Von Hanxleden, Steffen Weik, Stefan Zeug Entwurf einer domänenspezifischen Sprache für elektronische Stellwerke Andreas Mertgen Generic Rolesfor Increased Reuseability Harald Cichos,Malte Lochau, Sebastian Oster, Andy Schürr Reduktion von Testsuiten für Software-Produktlinien Erfahrungsberichte Mario Winter, Karin Vosseberg, Andreas Spillner, Peter Haberl Softwaretest-Umfrage Erkenntnisziele, Durchführung und Ergebnisse Daniel Merschen, Yves Duhr, Thomas Ringler, Bernd Hedenetz, Stefan Kowalewski Model-Based Analysis of Design Artefacts Applying an Annotation Concept Christian Hopp, Fabian Wolf, Holger Rendel, Bernhard Rumpe Einführung eines Produktlinienansatzes in die automotive Softwareentwicklung am Beispiel von Steuergerätesoftware

14 Workshops Frank Huch, Janis Voigtländer 5. Arbeitstagung Programmiersprachen (ATPS 2012) Birgitta König-Ries, Volkmar Pipek, Jens Pottebaum, Stefan Strohschneider IT-Unterstützung für Public Safety & Security: Interdisziplinäre Anforderungsanalyse, Architekturen und Gestaltungskonzepte (IT4PSS 2012) Andreas Birk,Florian Markert, Sebastian Oster Produktlinien im Kontext: Technologie, Prozesse, Business und Organisation (PIK 2012) Michaela Huhn, Stefan Gerken, Carsten Rudolph Zertifizierung und modellgetriebene Entwicklung sicherer Software (ZeMoSS 2012)

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16 Eingeladene wissenschaftliche Vorträge

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18 Herausforderungen und neue Technologien zurverifikation nebenläufiger verteilter Systeme Thomas Santen Microsoft Research Advanced Technology Labs Europe Europäisches Microsoft Innovations Center GmbH Ritterstrasse Aachen Abstract: Nebenläufige und verteilt arbeitende Softwaresysteme sind nicht neu. Betriebssysteme sind seit mehr als 20 Jahren inhärent nebenläufig. Verteilte Datenbanken werden mindestens genauso lange erfolgreich eingesetzt. Neue Entwicklungen wie Multi- und Many-Core Architekturen, Cloud und Ubiquitous Computing oder die immer größer werdenden Anforderungen an Performanz, Energieeffizienz und andere Qualitätseigenschaften stellen jedoch die Effektivität etablierter Qualitätssicherungstechniken in Frage. Insbesondere ist systematisches Testen immer weniger geeignet,die erforderliche Qualität solcher Systeme adäquat und kosteneffizient zu sichern. In dieser Situation stellen leistungsfähige modellbasierte und formale Verifikationstechniken eine attraktive Alternative dar. Der Vortrag zeigt, wie diese Techniken bereits heute in die industrielle Praxis der Softwareproduktion wirken. 17

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20 Die Multicore-Transformation und ihre Herausforderung an die Softwaretechnik Walter F. Tichy Institut für Programmstrukturen und Datenorganisation Karlsruher Institut für Technologie Am Fasanengarten Karlsruhe Abstract: Mehrkern-Rechner sind im Begriff, zu übernehmen. Mobiltelefone werden bereits mit Doppelprozessoren ausgestattet, Tablettrechner mit vier Prozessoren, PCs mit acht und mehr, Server mit Dutzenden, und GPUs mit einem halben Tausend. Diese Entwicklung stellt die Softwaretechnik vor die Herausforderung, parallele Software mit der gleichen Qualität und zu gleichen Kosten wie sequentielle herzustellen. Dieser Vortrag diskutiert einige der Forschungsthemen, die auf diese Herausforderung reagieren. Zu aller erst ist die Frage nach der Parallelisierungsmethodik: Wie findet man die Komponenten in neuer oder existierender Software, die von Parallelisierung profitieren? Was sind brauchbare Parallelisierungsmuster, Algorithmen, Daten- und Steuerungsstrukturen? Was sind geeignete Programmiersprachenerweiterungen, um Parallelität verständlich auszudrücken? Wie entdeckt man Parallelisierungsfehler oder vermeidet sie von vorne herein? Wie findet man eine gute Konfiguration der vielen neuen Parameter, die man in einem parallelen Programm berücksichtigen muss? Wie garantiert man Leistung? Und schließlich: Wie kommt man mit der zunehmenden Heterogenität der Prozessoren zurecht? Ohne Anspruch auf Vollständigkeit zeigt dieser Vortrag einige Forschungsrichtungen auf, die helfen können, Parallelprogrammierung zur Routine zu machen. 19

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22 Developing Physiological Computing Systems: Challenges and Solutions Extended Abstract AndreasSchroeder, Martin Wirsing Institut für Informatik Ludwig-Maximilians-Universität München Oettingenstr München {andreas.schroeder, Abstract: Today s computingsystems provideall kinds of media output to the users includingpictures, sounds, lights and visual animations; but user input is mostlyrestricted tonarrow, time-consuminginput modes such as mouse, touch screen or keyboard, even ifthe systemcould sense this information implicitly from the human body. Physiological computing allows one to re-balance this information asymmetry by consideringalso physiological data of theusers as input duringon-line processing. Using such inputs, physiological computingsystems are becoming able to monitor, diagnose and respond to the cognitive, emotional and physical states of persons in real time. In this paper wegive anintroduction to physiological computing, discussthe challenges that typicallyarise when developing physiological computingsystems, and solutions that we found promising in the creation of case studies. More specifically, we discussthe concepts software frameworks should incorporate to provide guidance in the development process: component-orientation, data processing support, anddistribution support were found useful when dealingwith physiological computing systems, where real-time sensing, on-line data processing, actuator control, context-awareness and self-adaptation areinvolved. Furthermore, wediscuss why agile software development methodologies seem appropriate to structure development efforts involvingphysiological computing. Finally, we cover the issues in validation and verification of physiological computingsystems that arise fromthe characteristics of the application domain, anddiscusshow empirical validation throughpsychological experiments, software validation and verification can interact with each other in the domain of physiological computing. 1. Introduction Today's computing systems and infrastructures are constantly failing to satisfy the increasing expectations of everyday users. This inability has several causes, one of which is the asymmetry and shortcomings of current communication channels between computer systems and users [No07].While the output of computer systems features a high bandwidth (visuals, audio, even tactile vibrations are used as output channels), the input to computers is still fairly limited. Even though touch screens are beginning to 21

23 enter everyday use through smartphones and tablets, communication paths that are used in human-to-human interactions through facial expression and gesture are not available in human-computer interaction. Instead, computers mostly offer keyboard and mouse as input devices with speech input just now being added to the picture. Together with touch screens and general forms ofbutton-pushing, theyconstitute virtually the complete set of input possibilities that modern computer systems offer. It is a very limited input channel, still, and entails that computer systems remain unaware of the environment, the state of the user and his goals. This unawareness is often perceived as stubbornness and dullness. As users learntoadapt to this behaviour oftheir computer, anodd phenomenon can be observed: the command chain between computer and user is virtually inverted [No07, Fa09]. The flexible and smart user subdues himself to the obstinate and dense computer in order toachieve hisown goal with the help of the limited machine available. Giving the computer awareness of the context and user will alleviate the imbalance of communication between user and machine and the entailed inversion of command. Computers that are aware of the user and the environment can be made able to detect their own wrong-doings easier and gain the possibility to compensate their actions. Extending the communication bandwidth from the user to the computer also promises to offer means for capturing motivations and goals of the user even on a subconscious level. This would allow to foresee a user's decision and conflicts with the current system operation and to adjust the system operation smoothly, making interaction with the everyday computing facilities of the user more enjoyable. This paper isbased on the results ofthe EU project REFLECT [IST ; and the PhD thesis of the first author [Sc12]. We start with giving an overview on physiological computing and its challenges in Section 2.The specifics of data processing, abstraction, and the creation of adaptation logic as feedback loops are discussed in Sections 2.2, 2.3, and 2.4 respectively. Before detailing these issues, we present a case study to which we contributed in its development: the Intelligent Co-Driver is introduced insection 2.1. InSection 3, we discuss how a software framework can support the development of physiological computing systems. In this discussion, we present engineering concepts that were found to be useful in the creation of the Intelligent Co-Driver; we take our insights from the construction of the REFLECT framework [Wi11, Sc12] that was widely adopted in the creation of this physiological computing system. Insights on the benefits of a component-based approach (Sec. 3.1), support for data processing (Sec. 3.2) and distribution (Sec. 3.3) are elaborated. Beyond software framework support, we also discuss how the development of physiological computing systems can benefit from an agile approach in Section 4.We specifically elaborate on Scrum as software development approach for physiological computing systems in Section 4.1. In Section 4.2, we also discuss the challenges of validation and verification that arise in physiological computing and how empirical validation from psychological research can be leveraged and interplay with software and system development efforts. Finally, we present related work in Section 5 and conclude in Section6. 22

24 2. Physiological Computing Physiological computing [Fa09] is a relatively new area of research that tries to provide more input to computing systems namely, physiological input in order to alleviate the communication asymmetry that state-of-the-art computing systems feature. It finds itself at the intersection between artificial intelligence, human-computer interaction and applied psychology. The term of physiological computing itself is integrative since it denotes all research about computing systems that measure physiological signals from the human body inwith the intent of processing it in real-time and leverage it in on-line operation; physiological computing is defined by the continuous on-line processing of physiological data. The continuous stream of processed data canbe used to create a more complete picture of the operational context of a system especially of an assistive system and can enable continuous and discrete adaptation and improvement of the operational environment of their users, thereby improving the user s environment. As a physiological computing system monitors the user s state that it improves, it creates a feedback loop involving both human users and computers. This feedback loop has therefore been coined the bio-cybernetic loop [SF09]. Applications featuring this feedback loop can produce a more accurate representation of the user s operational context through synthesis of information available through the computing infrastructure and environmental sensors, and information deducible from sensors placed on the human body (see Figure 1). Figure 1: Bio-cybernetic feedback loop structure While the definition of physiological computing does not specify any kind of computing infrastructure it should be running on, a convenient infrastructure for physiological computing are not desktop computers, but computing infrastructures that permeate the environment and are capable of controlling and altering parameters of the environment such as illumination, temperature, and background music, as well as providing 23

25 contextual information directly to the user. Those infrastructures also have the benefit of allowing access to physiological parameters, as part of the infrastructure may become wearable as gadgets or jewels, integrated in clothes and even in the human body [BC00]. At the same time, a computing infrastructure that offers software controls for a significant part of the environment promises to provide meaningful means for improving the experience of the user. In physiological computing, there are a number of challenges that have to be faced with in the creation of new systems. Before discussing them in Sections 2.2, 2.3 and 2.4, we first give an example of a physiological computing application in the automotive domain: the intelligent co-driver. 2.1 The Intelligent Co-Driver The intelligent co-driver isanexample physiological computingsystem that was realized within the REFLECT project ([RV11], also named the REFLECT Automotive Demonstrator). The goal in the creation of this physiological computing system was to construct a supportive environment for the driver that mimics the behaviour of a codriver taking care of the well-being of the driver. The intelligent co-driver should extend the driver s awareness for the driving situation as well as for his own well-being and his actions in a non-obtrusive and discrete way. The intelligent co-driver therefore consists of three parallel bio-cybernetic feedback loops that monitor and guide the driver s cognitive load, emotions, and physical comfort. Figure 2: Intelligent co-driver cockpit deployment The Effort loop is concerned with determining the mental workload of the driver and adjusting the driver's tasks accordingly. In the automotive demonstrator, the effort loop should reduce the number of secondary tasks the driver is asked to perform, i.e. in the automotive context, incoming mobile phone calls must be suppressed, and music currently playing is faded out to allow the driver to focus on the (currently demanding) primary task of driving. The indicators for mental workload that were usable in the automotive setting were determined to be heart rate and heart rate variability. High mental workload translates to a slightly increased heart rate, and a decrease in heart rate 24

26 variability (i.e. the heart beats become more regular under high mental workload) [Mu92]. The Emotion loop is involved in managing the emotion of the driver according to a driver-selected goal. The goal state can be selected from the following three emotional goal states (for simplicity of the user interface): energetic, neutral, and relaxed. The emotional loop continuously monitors the current emotional state of the driver, and uses music from the driver's own music database to guide him tothe desired goal state. For monitoring the driver's emotion, skin conductance level and skin temperature level features were selected that must be implemented in the demonstrator. Furthermore, the emotional loop requires the creation of a music database that stores the song effects on the driver, and a music player that selects songs according to the recorded song effects and the selected target mood. The concept of emotional control through personalised music, and the filtering processes required to detect the effects on the emotional state of the user are described by Janssen et al. in [Ja09]. The Comfort loop tracks the physical comfort the user is experiencing, and isable to adjust the driver seat moderately to improve on the sitting posture and stability of the driver. Physical comfort of the driver is tracked by the pressure pattern the driver is exerting on the seat, as well as by measuring the accelerations (longitudinal, lateral, and vertical) that the driver is experiencing through either fast driving or driving on a rough road [Be10, Be11]. Improving the sitting posture is achieved by inflating or deflating cushions that are available in the driver seat. Inflating the cushions result in more stabilised sitting, while deflated cushions allow more freedom to move. Additionally, the comfort loop monitors the driver's alertness to the road and should give indications of the user's drowsiness. Drowsiness is detected through eye blink duration, as blink durations increase significantly withdriver drowsiness [Ca03]. The intelligent co-driver was implemented into the cockpit of a Ferrari California car (see Figure 2), during the REFLECT project,and evaluated and demonstrated at the end of the project in spring In the creation ofthe intelligent co-driver, a number of technical challenges were faced that we describe in the following. 2.2 Input Data A first task in constructing physiological computing systems is creating processing code for physiological input data. The challenges here are dealing with high sampling rates, noise and interference inthe signals, and addressinginterpersonal variations. Raw physiological data comes at sampling rates between 30 Hz (for skin conductance or skin temperature signals, for example) and 10 khz (for EEG and ECG, for example). Due to the high sampling rate, some input signals require fast pre-processing code written inassembly orc,before it can be fed at lower sampling rates into systems written in high-level languages such as Java, C++, or Python. For example, skin conductance and skin temperature signals can be easily treated in Java directly, while ECG signals are normally pre-processed in C or assembly, and only extracted features (suchas heart beats) are handled at muchlower sampling rates in high-level languages. 25

27 Physiological input data, being physical data, also features varying levels of noise and interference that needs to be cleaned, or detected and accounted for. For example, skin conductance and ECG signals are significantly affected by sensor movements: the skin conductance level measured depends heavily on the skin trajectory length, and loose contacts create epochs of missing signals. In comparison, contact skin temperature sensorsare fairly robust with respect to movement. Finally, the absolute input values and amplitudes related to psychological reactions may vary significantly from person to person [Pi97] and may be heavily dependent on sensor placements. For example, skin conductance levels are known to be heavily dependent on individuals and placement of sensors, and therefore only relative values are used. Skin conductance is often compared to a baseline giving a relative scale through both mean and standard deviation ofthe baseline. The current skin conductance value is then normalized, i.e. given as the number of standard deviations it departs from the mean of the baseline. Heart beats on the other hand feature no dependence on sensor placement and a relatively low level of interpersonal variation. Here, recording a prolonged baseline of a person s heart rate and adjusting thresholds to this baseline may be sufficient for taking interpersonal variations into account. 2.3 Abstraction Once the issues of processing the raw input signals are addressed, the next step in the design of a bio-cybernetic feedback loop involves the creation of the proper abstraction from the input signals. Here, the current state of the user needs tobe inferred from the available data and transformed into a psychological model to represent the user state. The user state representation must include all information needed for decision-making in the bio-cybernetic loop. The psychological model may for example define that the emotional state of the user must be captured in terms of a valence scale ranging from -1 to 1(negative or positive emotion), and arousal scale ranging from 0to 1(calm or energetic), as in the case of the emotional loop of the Intelligent Co-Driver (cf. Sec. 2.1). The creation ofthe current user psychological state from physiological input features is however not achievable through feature extraction and direct mapping [Fa09]; A signal in physiological input data can be produced byseveral internal body processes. An increase in heart rate for example can be due to either physical exercise, or due to high mental workload and cognitive engagement. A decrease in skin temperature may be due to a positively valenced emotion, or due to a decrease in environmental temperature. Mapping from features extracted fromphysiological input data to psychological state is a complex process requiring careful consideration of input quality, means for associating signals to their cause, and accounting for the uncertainty in this association. In this abstraction process, techniques from artificial intelligence can be used to great benefits. In the mapping from skin temperature and skin conductance to emotional valence and energy, for example, Bayesian networks were used successfully to account for the uncertainty through probabilistic weighting. 26

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